Towards a Flexible Model of Word Meaning

Magnus Sahlgren

We would like to build a model of semantic knowledge that have the capacity to acquire and represent semantic information that is ambiguous, vague and incomplete. Furthermore, the model should be able to acquire this knowledge in an unsupervised fashion from unstructured text data. Such a model needs to be both highly adaptive and very robust. In this submission, we will first try to identify some fundamental principles that a flexible model of word meaning must adhere to, and then present a possible implementation of these principles in a technique we call Random Indexing. We will also discuss current limitations of the technique and set the direction for future research.


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